We also benchmark popular instance segmentation methods on iShape and find their performance degrades significantly. To this end, we introduce a stronger baseline considering irregular shape in this paper, which explicitly combines perception and reasoning. Our key insight is to simulate how a person identifies an irregular object. Taking the wire shown in Figure REF  for example, one natural way is to start from a local point and gradually expand by following the wire contour and figure out the entire object. The behavior of such “following the contour” procedure is a process of continuous iterative reasoning based on local clues, which is similar to the recent affinity-based approaches [1]}, [2]}. Under such observation, we propose a novel affinity-based instance segmentation baseline, called ASIS, which includes principles of generating effective and efficient affinity kernel based on dataset property to solve Arbitrary Shape Instance Segmentation. Experimental results show that the proposed baseline outperforms popular methods by a large margin on iShape.
